The Digital Frontier: A Narrative Review of Bio-Digital Twins, AI-Driven Orthodontics, and Ethical Clinical Integration

Review Article

Abstract

The transition from static digital records to dynamic Bio-Digital Twins is reshaping orthodontics by combining anatomical and functional data to simulate physiological behavior and improve diagnostic accuracy. This narrative review examines how artificial intelligence, including dynamic Bio-Digital Twins, can predict treatment duration, identify patients at risk of unplanned prolongation, and support personalized decision-making. A search of PubMed, ScienceDirect, Scopus, and Google Scholar was performed, focusing on digital workflows, precision orthodontics, aligner biomaterials, patient-reported outcomes, teleorthodontics, and the ethics of explainable AI. The evidence indicates that dynamic twins are more informative than conventional images because they integrate functional and biological data, while machine learning models estimate treatment time from clinical, imaging, and behavioral variables. Genetic, epigenetic, and multi-omics data further advance precision orthodontics. Successful implementation depends on high-quality data, explainable AI, reduction of algorithmic bias, and continued clinician oversight so that computational tools support rather than replace clinical judgment.